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Local influence in null intercept measurement error regression under a student_t model

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  • Filidor Labra
  • Reiko Aoki
  • Heleno Bolfarine

Abstract

In this paper we discuss the application of local influence in a measurement error regression model with null intercepts under a Student_t model with dependent populations. The Student_t distribution is a robust alternative to modelling data sets involving errors with longer than Normal tails. We derive the appropriate matrices for assessing the local influence for different perturbation schemes and use real data as an illustration of the usefulness of the application.

Suggested Citation

  • Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 723-740.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:723-740
    DOI: 10.1080/02664760500079639
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    1. Berkane, Maia & Kano, Yutaka & Bentler, Peter M., 1994. "Pseudo maximum likelihood estimation in elliptical theory: Effects of misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 18(2), pages 255-267, September.
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    3. Fernández, C. & Steel, M.F.J., 1997. "Multivariate Student -t Regression Models : Pitfalls and Inference," Discussion Paper 1997-08, Tilburg University, Center for Economic Research.
    4. Reiko Aoki & Jorge Achcar & Heleno Bolfarine & Julio Singer, 2003. "Bayesian analysis of null intercept errors-in-variables regression for pretest/post-test data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(1), pages 3-12.
    5. Manuel Galea & Heleno Bolfarine & Filidor Vilcalabra, 2002. "Influence diagnostics for the structural errors-in-variables model under the Student-t distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1191-1204.
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    Cited by:

    1. V. G. Cancho & Reiko Aoki & V. H. Lachos, 2008. "Bayesian analysis for a skew extension of the multivariate null intercept measurement error model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(11), pages 1239-1251.

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